An ideal national resistance monitoring program should deliver a precise estimate of the resistance situation for a given combination of bacteria and antimicrobial at a low cost. To achieve this, decisions need to be made on the number of samples to be collected at each of different possible sampling points. Existing methods of sample size calculation can not be used to solve this problem, because sampling decisions do not only depend on the prevalence of resistance and sensitivity and specificity of resistance testing, but also on the prevalence of the bacteria, and test characteristics of isolation of these bacteria. Our aim was to develop a stochastic simulation model that optimized a national resistance monitoring program, taking multi-stage sampling, imperfect sensitivity and specificity of diagnostic tests, and cost-effectiveness considerations into account. The process of resistance testing of Campylobacter spp. isolated from cloacal swab samples from poultry was modeled using a Markov Chain Monte Carlo model. Different sampling scenarios on the number of flocks to be tested, the number of birds from each flock, and the number of campylobacter colonies submitted to susceptibility testing were evaluated regarding the precision of the resulting prevalence estimate. Precision of the prevalence estimate was defined as the absolute difference between apparent and true prevalence of resistance. A partial budget approach was utilized to find the most cost-effective combination of samples to obtain a defined precision of the prevalence estimate. For a sampling scenario testing 100 flocks, five birds per flock, and one campylobacter colony per sample, the median error of the prevalence estimate was 2.5%, and 95% of the simulations resulted in an error of 7% or less. When the total number of samples was kept constant, maximizing the number of flocks tested, and only testing one bird per flock resulted in the most precise prevalence estimate. Submitting more than one campylobacter colony to resistance testing did not improve the prevalence estimate. Partial budget analysis indicated that the most cost-effective strategy was testing of two birds per flock, and submitting one colony per sample to resistance testing.